Source code for moe.views.gp_next_points_pretty_view

# -*- coding: utf-8 -*-
"""A class to encapsulate 'pretty' views for ``gp_next_points_*`` endpoints; e.g., :class:`moe.views.rest.gp_next_points_epi.GpNextPointsEpi`.

Include:

    1. Class that extends :class:`moe.views.optimizable_gp_pretty_view.GpPrettyView` for next_points optimizers

"""
import numpy

import moe.optimal_learning.python.cpp_wrappers.expected_improvement
from moe.optimal_learning.python.cpp_wrappers.expected_improvement import ExpectedImprovement
from moe.optimal_learning.python.python_version.expected_improvement import ExpectedImprovement as PythonExpectedImprovement
from moe.optimal_learning.python.repeated_domain import RepeatedDomain
import moe.optimal_learning.python.python_version.optimization as python_optimization
from moe.optimal_learning.python.timing import timing_context
from moe.views.gp_pretty_view import GpPrettyView
from moe.views.optimizable_gp_pretty_view import OptimizableGpPrettyView
from moe.views.schemas.gp_next_points_pretty_view import GpNextPointsRequest, GpNextPointsResponse
from moe.views.utils import _make_gp_from_params, _make_domain_from_params, _make_optimizer_parameters_from_params, _make_mvndst_parameters_from_params


EPI_OPTIMIZATION_TIMING_LABEL = 'EPI optimization time'


[docs]class GpNextPointsPrettyView(OptimizableGpPrettyView): """A class to encapsulate 'pretty' ``gp_next_points_*`` views; e.g., :class:`moe.views.rest.gp_next_points_epi.GpNextPointsEpi`. Extends :class:`moe.views.optimizable_gp_pretty_view.GpPrettyView` with: 1. gaussian_process generation from params 2. Converting params into a C++ consumable set of optimizer parameters 3. A method (compute_next_points_to_sample_response) for computing the next best points to sample from a gaussian_process """ request_schema = GpNextPointsRequest() response_schema = GpNextPointsResponse() _pretty_default_request = { "num_to_sample": 1, "gp_historical_info": GpPrettyView._pretty_default_gp_historical_info, "domain_info": { "dim": 1, "domain_bounds": [ { "min": 0.0, "max": 1.0, }, ], }, }
[docs] def compute_next_points_to_sample_response(self, params, optimizer_method_name, route_name, *args, **kwargs): """Compute the next points to sample (and their expected improvement) using optimizer_method_name from params in the request. .. Warning:: Attempting to find ``num_to_sample`` optimal points with ``num_sampled < num_to_sample`` historical points sampled can cause matrix issues under some conditions. Try requesting ``num_to_sample < num_sampled`` points for better performance. To bootstrap more points try sampling at random, or from a grid. :param request_params: the deserialized REST request, containing ei_optimizer_parameters and gp_historical_info :type request_params: a deserialized self.request_schema object as a dict :param optimizer_method_name: the optimization method to use :type optimizer_method_name: string in :const:`moe.views.constant.NEXT_POINTS_OPTIMIZER_METHOD_NAMES` :param route_name: name of the route being called :type route_name: string in :const:`moe.views.constant.ALL_REST_ROUTES_ROUTE_NAME_TO_ENDPOINT` :param ``*args``: extra args to be passed to optimization method :param ``**kwargs``: extra kwargs to be passed to optimization method """ points_being_sampled = numpy.array(params.get('points_being_sampled')) num_to_sample = params.get('num_to_sample') num_mc_iterations = params.get('mc_iterations') max_num_threads = params.get('max_num_threads') gaussian_process = _make_gp_from_params(params) ei_opt_status = {} # TODO(GH-89): Make the optimal_learning library handle this case 'organically' with # reasonable default behavior and remove hacks like this one. if gaussian_process.num_sampled == 0: # If there is no initial data we bootstrap with random points py_domain = _make_domain_from_params(params, python_version=True) next_points = py_domain.generate_uniform_random_points_in_domain(num_to_sample) ei_opt_status['found_update'] = True expected_improvement_evaluator = PythonExpectedImprovement( gaussian_process, points_being_sampled=points_being_sampled, num_mc_iterations=num_mc_iterations, ) else: # Calculate the next best points to sample given the historical data optimizer_class, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params) if optimizer_class == python_optimization.LBFGSBOptimizer: domain = RepeatedDomain(num_to_sample, _make_domain_from_params(params, python_version=True)) expected_improvement_evaluator = PythonExpectedImprovement( gaussian_process, points_being_sampled=points_being_sampled, num_mc_iterations=num_mc_iterations, mvndst_parameters=_make_mvndst_parameters_from_params(params) ) opt_method = getattr(moe.optimal_learning.python.python_version.expected_improvement, optimizer_method_name) else: domain = _make_domain_from_params(params, python_version=False) expected_improvement_evaluator = ExpectedImprovement( gaussian_process, points_being_sampled=points_being_sampled, num_mc_iterations=num_mc_iterations, ) opt_method = getattr(moe.optimal_learning.python.cpp_wrappers.expected_improvement, optimizer_method_name) expected_improvement_optimizer = optimizer_class( domain, expected_improvement_evaluator, optimizer_parameters, num_random_samples=num_random_samples, ) with timing_context(EPI_OPTIMIZATION_TIMING_LABEL): next_points = opt_method( expected_improvement_optimizer, params.get('optimizer_info')['num_multistarts'], # optimizer_parameters.num_multistarts, num_to_sample, max_num_threads=max_num_threads, status=ei_opt_status, *args, **kwargs ) # TODO(GH-285): Use analytic q-EI here # TODO(GH-314): Need to resolve poential issue with NaNs before using q-EI here # It may be sufficient to check found_update == False in ei_opt_status # and then use q-EI, else set EI = 0. expected_improvement_evaluator.current_point = next_points # The C++ may fail to compute EI with some ``next_points`` inputs (e.g., # ``points_to_sample`` and ``points_begin_sampled`` are too close # together or too close to ``points_sampled``). We catch the exception when this happens # and attempt a more numerically robust option. try: expected_improvement = expected_improvement_evaluator.compute_expected_improvement() except Exception as exception: self.log.info('EI computation failed, probably b/c GP-variance matrix is singular. Error: {0:s}'.format(exception)) # ``_compute_expected_improvement_monte_carlo`` in # :class:`moe.optimal_learning.python.python_version.expected_improvement.ExpectedImprovement` # has a more reliable (but very expensive) way to deal with singular variance matrices. python_ei_eval = PythonExpectedImprovement( expected_improvement_evaluator._gaussian_process, points_to_sample=next_points, points_being_sampled=points_being_sampled, num_mc_iterations=num_mc_iterations, ) expected_improvement = python_ei_eval.compute_expected_improvement(force_monte_carlo=True) return self.form_response({ 'endpoint': route_name, 'points_to_sample': next_points.tolist(), 'status': { 'expected_improvement': expected_improvement, 'optimizer_success': ei_opt_status, }, })